| Meta (Instagram, Facebook) | |
| // Узлы | |
| 157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32 | |
| 157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38 | |
| 57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97 | |
| // Подсети | |
| 213.102.128.0/24 | |
| 204.15.20.0/22 | |
| 199.201.0.0/16 |
| #====================================================================== | |
| To make an informed decision on purchasing an XDR product, you need a structured approach to evaluating vendors and ensuring the solution meets your security needs. Here's how you can organize the process effectively: | |
| 1. Define Evaluation Goals & Success Criteria | |
| Establish clear objectives for adopting an XDR solution (e.g., better threat detection, improved response automation). | |
| Identify key security gaps that need addressing. | |
| Define measurable success criteria for the evaluation (e.g., ease of integration, accuracy of threat detection, response time). |
| #!/usr/bin/env bash | |
| set -euo pipefail | |
| DOMAIN="${1:-example.com}" | |
| RELEASE="${CC_RELEASE:-cc-main-2026-jan-feb-mar}" | |
| CACHE="${HOME}/.cache/cc-backlinks/${RELEASE}" | |
| BASE="https://data.commoncrawl.org/projects/hyperlinkgraph/${RELEASE}/domain" | |
| VERTICES="${CACHE}/domain-vertices.txt.gz" | |
| EDGES="${CACHE}/domain-edges.txt.gz" |
See Boris’s post: these tips come from the Claude Code team, and there’s no single “right” setup—experiment and keep what works.
git worktree so each session has its own isolated working directory.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| 4.0.0: | |
| Windows: https://desktop.docker.com/win/main/amd64/67817/Docker%20Desktop%20Installer.exe | |
| Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/67817/Docker.dmg | |
| Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/67817/Docker.dmg | |
| release_date: '2021-08-31' | |
| 4.0.1: | |
| Windows: https://desktop.docker.com/win/main/amd64/68347/Docker%20Desktop%20Installer.exe | |
| Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/68347/Docker.dmg | |
| Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/68347/Docker.dmg | |
| release_date: '2021-09-13' |
Workspace: local-frontend
เครื่องมือ: VS Code + GitHub Copilot + claude-context MCP
Stack: Next.js 14+ (App Router) + TypeScript + Tailwind CSS + Redux Toolkit + next-intl
📘 ไฟล์นี้ครอบคลุมงานฝั่ง Frontend ทั้งหมด
สำหรับงาน Backend → ดูTeam_Playbook_BE.md
สำหรับ Full-Stack Feature → ต้องทำ หมวด 0: API Contract ก่อนเสมอ